Adaptive Neural Tracking Control for High-Order Nonlinear Systems With Unmodeled-Dynamics and Sensor-Fault
Huanqing Wang, Jiawei Ma, Huaguang Zhang
Abstract
In this brief, the adaptive neural sensor-fault tolerant control issue of high-order nonlinear systems (HONs) with unmodeled-dynamics is discussed. The small-gain theorem (SGT) is introduced to investigate the boundedness of the HONs due to the existence of unmodeled-dynamics that may be unbounded. Further, based on radial basis function neural networks (RBF NNs), an adaptive neural controller is proposed to guarantee all signals of HONs to be bounded. Finally, the simulation example can demonstrate effectiveness of proposed scheme.
Topics & Concepts
Control theory (sociology)Nonlinear systemTracking (education)Dynamics (music)Computer scienceAdaptive controlControl (management)Control engineeringFault detection and isolationEngineeringArtificial intelligencePhysicsAcousticsQuantum mechanicsPsychologyPedagogyAdaptive Control of Nonlinear SystemsAdaptive Dynamic Programming ControlFault Detection and Control Systems